In recent years, neural-network based models have achieved the state of the art for a wide range of natural language processing tasks, including SF and IC. Various neural architectures have been experimented on SF and IC, including RNN-based [1]} and attention-based [2]} approaches, till the more recent transformers models [3]}. Input representations have also evolved from static word embeddings [4]}, [5]}, [6]} to contextualized word embeddings [7]}, [8]}. Such progress allows to better address dialogue phenomena involving SF and IC, including context modeling, handling out-of-vocabulary words, long-distance dependency between words, and to better exploit the synergy between SF and IC through joint models.
In addition to rapid progresses in the research community, the demand for commercial conversational AI is also growing fast, shown by a variety of available solutions, such as Microsoft LUIS, Google Dialogflow, RASA, and Amazon Alexa. These solutions also use various kinds of semantic frame representations as part of their framework.
